Legal claims defining the scope of protection, as filed with the USPTO.
1. A system for prediction of protein-ligand bioactivity comprising: a computing device comprising a memory and a processor; a point-cloud based bioactivity module comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions causes the computing device to: receive a molecular structure file, wherein the molecular structure file comprises molecular and structural information about at least one protein and one ligand; generate a graph-based neural network of the protein, wherein edges of the graph-based neural network of the protein are determined using the molecular structure file; generate a graph-based neural network of the ligand, wherein edges of the graph-based neural network of the ligand are determined using the molecular structure file; concatenate a set of vectors from both the graph-based neural network of the protein and the graph-based neural network of the ligand; perform restricted-cross-attention learning on the concatenated vectors; generate a single feature vector from the restricted-cross-attention learning; use the single feature vector in a feed-forward neural network to produce one or more outputs selected from the group consisting of active or inactive classification, crystalline-structure similarity, and regression analysis; and use the one or more outputs to produce one or more bioactivity predictions about one or more protein-ligand pairs.
2. The system of claim 1 , wherein docking simulations are performed on the protein and the ligand from the molecular structure file if the protein and the ligand are coupled.
3. The system of claim 1 , wherein the vector from the graph-based neural network of the protein comprises atoms within 4 angstroms of the ligand if the protein and the ligand are coupled.
4. The system of claim 1 , wherein the vector from the graph-based neural network of the protein comprises atoms of a binding pocket if the protein and the ligand are decoupled.
5. The system of claim 1 , wherein the crystalline-structure similarity prediction is used to determine the legitimacy of the one or more bioactivity predictions.
6. The system of claim 1 , wherein the graph-based neural network of the protein and the graph-based neural network of the ligand are based on one or more transformer convolution classifiers.
7. The system of claim 1 , wherein the one or more outputs are used with a loss function to produce a trained model for bioactivity prediction.
8. The system of claim 1 , wherein the machine learning model is a transformer convolution classifier.
9. The system of claim 1 , further comprising an output of a three-dimensional visualization of the one or more protein-ligand pairs.
10. The system of claim 9 , wherein the three-dimensional visualization comprises features of molecular interaction properties.
11. A method for prediction of protein-ligand bioactivity comprising the steps of: receiving a molecular structure file, wherein the molecular structure file comprises molecular and structural information about at least one protein and one ligand; generating a graph-based neural network of the protein, wherein edges of the graph-based neural network of the protein are determined using the molecular structure file; generating a graph-based neural network of the ligand, wherein edges of the graph-based neural network of the ligand are determined using the molecular structure file; concatenating a set of vectors from both the graph-based neural network of the protein and the graph-based neural network of the ligand; performing restricted-cross-attention learning on the concatenated vectors; generating a single feature vector from the restricted-cross-attention learning; using the single feature vector in a feed-forward neural network to produce one or more outputs selected from the group consisting of active or inactive classification, crystalline-structure similarity, and regression analysis; and using the one or more outputs to produce one or more bioactivity predictions about one or more protein-ligand pairs.
12. The method of claim 11 , wherein docking simulations are performed on the protein and the ligand from the molecular structure file if the protein and the ligand are coupled.
13. The method of claim 11 , wherein the vector from the graph-based neural network of the protein comprises atoms within 4 angstroms of the ligand if the protein and the ligand are coupled.
14. The method of claim 11 , wherein the vector from the graph-based neural network of the protein comprises atoms of a binding pocket if the protein and the ligand are decoupled.
15. The method of claim 11 , wherein the crystalline-structure similarity prediction is used to determine the legitimacy of the one or more bioactivity predictions.
16. The method of claim 11 , wherein the graph-based neural network of the protein and the graph-based neural network of the ligand are based on one or more transformer convolution classifiers.
17. The method of claim 11 , wherein the one or more outputs are used with a loss function to produce a trained model for bioactivity prediction.
18. The method of claim 11 , wherein the machine learning model is a transformer convolution classifier.
19. The method of claim 11 , further comprising an output of a three-dimensional visualization of the one or more protein-ligand pairs.
20. The method of claim 19 , wherein the three-dimensional visualization comprises features of molecular interaction properties.
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February 22, 2022
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